AIPatient KG: MIMIC-III and CORAL Electronic Health Records based Patient Knowledge Graph

This study integrates the MIMIC-III and CORAL electronic health records into
knowledge graphs to enhance their utility for advanced medical analysis and
decision-making. MIMIC-III contains comprehensive data from over 40,000
patients, while CORAL focuses on oncology-specific information from 40
patients, aiding in complex medical reasoning. We used a LLM (Large Language
Model)-based Named Entity Recognition approach to extract relevant medical
information from these datasets, independently verified by domain experts, and
constructed the AIPatient and CORAL Knowledge Graph in Neo4j. This graph
supports the AIPatient system, which simulates patient interactions for
advanced decision support. Additionally, we introduce MIMIC-III and CORAL
Question and Answering sets, which are created for evaluating system
performance such as accuracy, robustness and stability.

本研究将MIMIC-III与CORAL电子健康记录（electronic health records）整合为知识图谱（knowledge graphs），以提升其在先进医学分析与临床决策辅助中的应用效能。其中，MIMIC-III涵盖超4万名患者的多维度全面医疗数据，而CORAL则聚焦于40名患者的肿瘤学专属医疗信息，可辅助开展复杂医学推理任务。我们采用基于大语言模型（Large Language Model，LLM）的命名实体识别（Named Entity Recognition）方法，从上述数据集中提取相关医学信息，并经由领域专家独立核验，最终在Neo4j平台上构建了AIPatient与CORAL知识图谱。该图谱可为AIPatient系统提供支撑，该系统可模拟医患交互场景以实现高级临床决策辅助。此外，本研究还构建了MIMIC-III与CORAL问答数据集，用于评估系统的准确性、鲁棒性与稳定性等核心性能指标。

搜集汇总

背景与挑战

背景概述

该数据集是基于MIMIC-III和CORAL电子健康记录构建的患者知识图谱，包含两个子图谱：AIPatient KG（56名MIMIC-III患者）和CORAL KG（40名肿瘤患者）。数据集采用LLM进行医疗实体识别，构建了包含症状、病史、过敏等节点的结构化知识图谱，支持医疗AI推理、临床决策和医学教育应用。